Early Stage Fire Source Classification in Building using Artificial Intelligence

Abstract

Identification of burning smell is crucial because enables early fire recognition and avoidance. Based on this study, an early stage fire detection algorithm is offered via Probabilistic Neural Network (PNN). Experiments were conducted on seven generally accessible flammable materials and three building construction materials. All the materials were scorched in a vacuum oven at various temperature points, pushed out using vacuum pumps to be sniffed by the electronic nose. The experiments were done in a confined room with monitored temperature and humidity level. Standardised feature extractions of the smell print data were carried out prior to subjection of detection categorization. These aspects signify the odour pointers within the time frame. Experimental categorization outcomes indicate that the tuning of spread factor in PNN classifier has enhanced the precision of classification and delivered excellent reliability, irrespective of humidity variation and ambient temperature, the various gas concentration levels, exposure towards different heating temperature range and baseline sensor drift. The mean classification accuracy for this classification model has been identified at 94.18%.